Labatt Playbook Analysis

Social Media Analysis to support Quench new business proposal

Executive Summary

• Labatt has a small but loyal fanbase that interacts at a higher rate than other companies
• BudLight’ fanbase is driving their engagement numbers due to the large outsizing versus labatt
• Labatt needs to focus their message around what’s driving engagement in their sector and other sectors by other companies such as football.

Abstract

Introduction

Methods

Data Collection

Data was imported using the \data_gathering.RMD script. See that script for details of collection.

Data Shaping

Taking in raw data and adding a parseable timestamp while filtering on the date and client_ids.

Function Definition

Define functions to create posts per day of week graphs, and timeseries of engagement line graphs.

Additional Data Shaping for Engagement

Shape data into vertical data formats.

Attaching package: ‘chron’

The following objects are masked from ‘package:lubridate’:

    days, hours, minutes, seconds, years

Results

Summary Statistics

• Lets start here with a table of summary statistics

[1] "tbl_df"     "tbl"        "data.frame"

Matrices plots of Engagement

First plot is aggregated engagement by content type. Second plot, it engagement by type for client(Labatt).

• As Bud Light and Michelob ULTRA are the to companies with the highest engagement, comparison of

• Looking at the engagement by content type we see that Labatt is garnering its most significant engagment on Photos, Video, and Links.

• [ ] TODO: we need to compare posting activity with engagement activity (scatter plot)

Summary Plots

Horizontal stacked bar chart for total engagement comparison of all companies

reorder_size <- function(x) {
  factor(x, levels = names(sort(table(x))))
}
p <- summary_stats %>%
  filter(Engagement != "Total.Posts") %>%
  ggplot(., aes(x = Company, y = Number, fill = Engagement)) +
  geom_bar(stat = "identity") +
  xlab("") + ylab("") +
  coord_flip()
plot(p)

• [ ] TODO: Create a scaled version of the stacked eng bar chart that is scaled by the number of fans for each bar.

Day of Week

Total posts per day of the week.

# without brand ID these are uninformative
for(i in seq_along(df_names)) {
  p <- day_of_week(df_names[i], client_names[i])
  plot(p)
}

p <- ggplot(data = all_companies_ts, aes(x = wday(timestamp, label = TRUE))) +
  geom_bar(aes(fill = ..count..)) +
  theme(legend.position = "none") +
  xlab("Day of the Week") + ylab("Number of Posts") +
  scale_fill_gradient(low = "midnightblue", high = "aquamarine4") + 
  facet_wrap(~from_name, ncol = 4) +
  ggtitle("Daily Posting Activity by brand")
plot(p)

• What is the total number of posts?

dowDat <- select(all_companies_ts, total_engagement,from_name, timestamp)
dowDat$dow <- wday(dowDat$timestamp, label=TRUE)
dowDat <- aggregate(total_engagement~dow+from_name, data=dowDat, FUN=mean)
p <- ggplot(dowDat, aes(x = dow, y = total_engagement)) +
  geom_bar(stat="identity", aes(fill = total_engagement)) + 
  facet_grid(~from_name) + 
  ggtitle('Engagements Per Day of Week') +
  theme(legend.position = "none") +
  xlab("Day of the Week") + ylab("Number of Engagements") +
  scale_fill_gradient(low = "midnightblue", high = "aquamarine4")
plot(p)

-[ ] TODO: Create a plot for Post by engagement graphics (scatter plot). To answer the question on days with lots of posts do we get lots of engagment.

• [] TODO: With that data we can ask what posts get the most engagment, we can look at top engagment and bottom engagements posts and what qualities they share or differ by.

• [X] Time of day visual break down?

Engagement by Time of Day (TOD)

Timeseries Engagement

Plots for the timeseries engagement line.

for(i in seq_along(df_names)) {
  p <- timeseries_engagement(client_names_proper[i])
  plot(p)
}

Initial Visualization of engagement over time on a line

Test viz, showed spike in enegagment for Bud Light in august 2016.

all_companies_ts <- all_companies_ts %>%
  filter(from_id %in% client_ids) %>%
  mutate(month = as.Date(cut(all_companies_ts$timestamp, breaks = "month")))
ggplot(all_companies_ts, aes(x = month, y = total_engagement)) +
  geom_line(aes(group = from_name, color = factor(from_name)))

all_companies_ts %>%
  select(from_name, month, total_engagement) %>%
  group_by(from_name,month) %>%
  summarise(totEng = sum(total_engagement)) %>%
  ggplot(., aes(x = month, y = totEng)) +
   geom_point(aes(color = from_name)) +
  geom_smooth(aes(color = from_name), se = FALSE)

all_companies_ts %>%
  select(from_name, month, total_engagement) %>%
  filter(from_name != "Bud Light" ) %>%
  filter(from_name != "Michelob ULTRA") %>%
  group_by(from_name,month) %>%
  summarise(totEng = sum(total_engagement)) %>%
  ggplot(., aes(x = month, y = totEng)) +
   geom_point(aes(color = from_name)) +
   geom_smooth(aes(color = from_name), se = FALSE) +
   ggtitle("Monthly Facebook Engagement w/o Bud & MichULTRA")

• This is an interesting drop of ~30% over the first 6 months of 2015. The brand has still not recovered from that reduction.
  

• What is different about the content during this period?

• Might be valuable to look back at the entire timeseries for periods of distinct dynamism.

Labatt Wordclouds

Removed filter because labatt does not have significant inflection point whereas previous analysis

labatt$timestamp <- date(labatt$timestamp)
labatt_clean_pre <- str_replace_all(labatt$message, "@\\w+", "")
labatt_clean_pre <- gsub("&amp", "", labatt_clean_pre)
labatt_clean_pre <- gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", labatt_clean_pre)
labatt_clean_pre <- gsub("@\\w+", "", labatt_clean_pre)
labatt_clean_pre <- gsub("[[:punct:]]", "", labatt_clean_pre)
labatt_clean_pre <- gsub("[[:digit:]]", "", labatt_clean_pre)
labatt_clean_pre <- gsub("http\\w+", "", labatt_clean_pre)
labatt_clean_pre <- gsub("[ \t]{2,}", "", labatt_clean_pre)
labatt_clean_pre <- gsub("^\\s+|\\s+$", "", labatt_clean_pre)
labatt_corpus_pre <- Corpus(VectorSource(labatt_clean_pre))
labatt_corpus_pre <- tm_map(labatt_corpus_pre, removePunctuation)
labatt_corpus_pre <- tm_map(labatt_corpus_pre, content_transformer(tolower))
labatt_corpus_pre <- tm_map(labatt_corpus_pre, removeWords, stopwords("english"))
labatt_corpus_pre <- tm_map(labatt_corpus_pre, removeWords, c("amp", "2yo", "3yo", "4yo"))
labatt_corpus_pre <- tm_map(labatt_corpus_pre, stripWhitespace)
pal <- brewer.pal(9,"YlGnBu")
pal <- pal[-(1:4)]
set.seed(123)
wordcloud(words = labatt_corpus_pre, scale=c(5,0.1), max.words=25, random.order=FALSE, 
          rot.per=0.35, use.r.layout=FALSE, colors=pal)

Point Graphs for Posts

Displays engagement per post to find outliers.

p <- ggplot(all_companies_ts, aes(x = month, y = total_engagement)) +
  geom_point(aes(color = from_name)) +
  xlab("Year") + ylab("Total Engagement") + 
  theme(legend.title=element_blank(), 
        legend.text=element_text(size=12), 
        legend.position=c(0.18, 0.77), 
        legend.background=element_rect(fill=alpha('gray', 0)))
plot(p)

Total Engagement Line

# q <- aggregate(all_companies_ts$total_engagement~all_companies_ts$month+
#                  all_companies_ts$from_name,
#                FUN=sum)
# 
# ggplot(q, aes(x = q$`all_companies_ts$month`, y = q$`all_companies_ts$total_engagement`)) +
#   geom_line(aes(color=q$`all_companies_ts$from_name`)) +
#   ylab("Total Engagement") + xlab("Year") +
#   theme(legend.title=element_blank(), 
#         legend.text=element_text(size=12), 
#         legend.position=c(0.18, 0.77), 
#         legend.background=element_rect(fill=alpha('gray', 0)))

Engagement by Company

### molson Content Over Time ###
t <- all_companies_ts %>%
  filter(., from_name == "Molson Canadian")
t <- data.frame(table(t$month, t$type))
t$Var1 <- date(t$Var1)
ggplot(t, aes(x = Var1, y = Freq, group = Var2)) +
  geom_line(aes(color=Var2)) +
  ggtitle('Molson Engagement') +
  xlab("Year") + ylab("Post Frequency") +
  theme(legend.title=element_blank(), 
        legend.text=element_text(size=12), 
        legend.position=c(0.18, 0.77), 
        legend.background=element_rect(fill=alpha('gray', 0)))

#TRISTEN'S GRAPHS!!
#Labatt Content Over Time
### Labatt Content Over Time ###
t <- all_companies_ts %>%
  filter(., from_name == "Labatt USA")
t <- data.frame(table(t$month, t$type))
t$Var1 <- date(t$Var1)
ggplot(t, aes(x = Var1, y = Freq, group = Var2)) +
  geom_line(aes(color=Var2)) +
  ggtitle('Labatt Facebook Activity') +
  xlab("Year") + ylab("Post Frequency") +
  theme(legend.title=element_blank(), 
        legend.text=element_text(size=12), 
        legend.position=c(0.18, 0.77), 
        legend.background=element_rect(fill=alpha('gray', 0)))

#Labatt Content Over Time
#MichelobULTRA Content Over Time ###
t <- all_companies_ts %>%
  filter(., from_name == "Michelob ULTRA")
t <- data.frame(table(t$month, t$type))
t$Var1 <- date(t$Var1)
ggplot(t, aes(x = Var1, y = Freq, group = Var2)) +
  geom_line(aes(color=Var2)) +
  ggtitle('Michelob ULTRA Engagement') +
  xlab("Year") + ylab("Post Frequency") +
  theme(legend.title=element_blank(), 
        legend.text=element_text(size=12), 
        legend.position=c(0.18, 0.77), 
        legend.background=element_rect(fill=alpha('gray', 0)))

• Is this true? TODO: Verify that these are the only content types for Molson.

#Labatt Content Over Time
#Bud Light Content Over Time ###
t <- all_companies_ts %>%
  filter(., from_name == "Bud Light")
t <- data.frame(table(t$month, t$type))
t$Var1 <- date(t$Var1)
ggplot(t, aes(x = Var1, y = Freq, group = Var2)) +
  geom_line(aes(color=Var2)) +
  ggtitle('Bud Light Engagement') +
  xlab("Year") + ylab("Post Frequency") +
  theme(legend.title=element_blank(), 
        legend.text=element_text(size=12), 
        legend.position=c(0.18, 0.77), 
        legend.background=element_rect(fill=alpha('gray', 0)))

Pulling #hastags

I found an example on Stackoverflow

Experiment with Hashtag extraction

# LabattUSA_timeline %>% 
#   filter()
# 
# 
# tweets <- LabattUSA_timeline$text
# match <- regmatches(tweets,gregexpr("#[[:alnum:]]+",tweets))
# 
# # Convert the list to a corpus
# # new_corpus <- as.VCorpus(new_list)  from Stackoverflow (http://stackoverflow.com/questions/34061912/how-transform-a-list-into-a-corpus-in-r)
# 
# new_corpus <- as.VCorpus(match)
# class(new_corpus)
# inspect(new_corpus)
# 
# EnsurePackage <- function(x) {
#   # EnsurePackage(x) - Installs and loads a package if necessary
#   # Args:
#   #   x: name of package
# 
#   x <- as.character(x)
#   if (!require(x, character.only=TRUE)) {
#     install.packages(pkgs=x, repos="http://cran.r-project.org")
#     require(x, character.only=TRUE)
#   }
# }
# 
# MakeWordCloud <- function(corpus) {
#   # Make a word cloud
#   #
#   # Args:
#   #   textVec: a text vector
#   #
#   # Returns:
#   #   A word cloud created from the text vector
#   
#   EnsurePackage("tm")
#   EnsurePackage("wordcloud")
#   EnsurePackage("RColorBrewer")
#   
#   corpus <- tm_map(corpus, function(x) {
#     removeWords(x, c("via", "rt", "mt"))
#   })
#   
#   ap.tdm <- TermDocumentMatrix(corpus)
#   ap.m <- as.matrix(ap.tdm)
#   ap.v <- sort(rowSums(ap.m), decreasing=TRUE)
#   ap.d <- data.frame(word = names(ap.v), freq=ap.v)
#   table(ap.d$freq)
#   pal2 <- brewer.pal(8, "Dark2")
#   
#   wordcloud(ap.d$word, ap.d$freq, 
#             scale=c(8, .2), min.freq = 3, 
#             max.words = Inf, random.order = FALSE, 
#             rot.per = .15, colors = pal2)
# }
# 
# MakeWordCloud(new_corpus)

Mosaic Plot Experiment

• [ ] TODO: Full timeseries of total eng by brand. (To look for seasonality) - if sports are a driver than seasonality might be important

# p <- unfiltered_ts %>%
#   summarise(jd = doy(timestamp)) %>%
#   group_by(jd) %>%
#   ggplot(aes(factor(jd),total_engagement)) +
#   geom_boxplot() + 
#   facet_grid(~ from_name)
# plot(p)

• [ ] Populate a table of top performing posts and low performing posts - Tristen can pull shot of tweets for discussion

• [ ] Create a data.frame with these columns brand, data, tweet, engagement (I think this is a subset of all_companies)

• [ ] summary table of brand, month, totEng, see examples:http://leonawicz.github.io/HtmlWidgetExamples/ex_dt_sparkline.html

all_companies_ts %>%
  select(from_name, timestamp, total_engagement) %>%
  group_by(from_name, month(timestamp), year(timestamp)) %>%
  summarise(count = n(), 
            engagement = sum(total_engagement)) %>%
  ggplot(., aes(y = log(engagement), x = log(count), colour = from_name)) +
  geom_point() +
  geom_smooth(se = FALSE, method = "lm") +
  #geom_smooth(se = FALSE)
  ggtitle("Engagement vs Post Acitivity")

all_companies_ts %>%
  #filter(from_name != "Bud Light" ) %>%
  #filter(from_name != "Michelob ULTRA") %>%
  select(from_name, timestamp, total_engagement) %>%
  group_by(from_name, month(timestamp), year(timestamp)) %>%
  summarise(count = n(),
            engagement = sum(total_engagement)) %>%
  ggplot(., aes(y = engagement, x = count, colour = from_name)) +
  geom_point() +
  geom_smooth(se = FALSE, method = "lm") +
  ggtitle("Engagement vs Post Acitivity") +
  ylab("Total Engagement") + xlab("Total Monthly Posts")

• There is a positive relationship between post activity (ie counts) and total engagement.

• [ ] TOD vs engagement similar to post activity vs Engagement

Kevins Questions

# load('processed_data/bud_fb.RData')
# bud$total_engagement <- rowSums(bud[,9:11])
# z <- bud %>%
#   arrange(desc(total_engagement))
# head(z)
# Updated upstream

Twitter

text_clean <- function(cleanliness) {
  cleanliness <- str_replace_all(cleanliness, "@\\w+", "")
  cleanliness <- gsub("&amp", "", cleanliness)
  cleanliness <- gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", cleanliness)
  cleanliness <- gsub("@\\w+", "", cleanliness)
  cleanliness <- gsub("[[:punct:]]", "", cleanliness)
  cleanliness <- gsub("[[:digit:]]", "", cleanliness)
  cleanliness <- gsub("http\\w+", "", cleanliness)
  cleanliness <- gsub("[ \t]{2,}", "", cleanliness)
  cleanliness <- gsub("^\\s+|\\s+$", "", cleanliness)
  return(cleanliness)
}
LabattUSA_timeline$sentiment <- lapply(text_clean(LabattUSA_timeline$text), get_nrc_sentiment)
labatt_sentiment <- data.frame('created' = LabattUSA_timeline$created,
                               'text' = LabattUSA_timeline$text,
                               'sentiment' = as.character(LabattUSA_timeline$sentiment))
labatt_sentiment$score <- get_sentiment(as.character(text_clean(labatt_sentiment$text))) %>% as.numeric()
labatt_sentiment %>%
  arrange(desc(score)) %>%
  select(created, score) %>%
  tail(5)

labatt_sentiment %>%
  ggplot(aes(as_date(created), score)) +
  geom_line(size = 1) +
  geom_smooth() +
  scale_color_manual(values = colourList) +
  scale_x_date(breaks = date_breaks("3 months"), labels = date_format("%Y-%b")) +
  scale_y_continuous(name = "Sentiment\n", breaks = seq(-5, 5, by = 1)) + theme_bw() +
  ggtitle('Labatt Sentiment')

Molson_Canadian_timeline$sentiment <- lapply(text_clean(Molson_Canadian_timeline$text), get_nrc_sentiment)
molson_sentiment <- data.frame('created' = Molson_Canadian_timeline$created,
                               'text' = Molson_Canadian_timeline$text,
                               'sentiment' = as.character(Molson_Canadian_timeline$sentiment))
molson_sentiment$score <- get_sentiment(as.character(text_clean(molson_sentiment$text))) %>% as.numeric()
molson_sentiment %>%
  arrange(desc(score)) %>%
  select(created, score) %>%
  tail(5)

molson_sentiment %>%
  ggplot(aes(as_date(created), score)) +
  geom_line(size = 1) +
  geom_smooth() +
  scale_color_manual(values = colourList) +
  scale_x_date(breaks = date_breaks("3 months"), labels = date_format("%Y-%b")) +
  scale_y_continuous(name = "Sentiment\n", breaks = seq(-5, 5, by = 1)) + theme_bw() +
  ggtitle('Molson Sentiment')

budlight_timeline$sentiment <- lapply(text_clean(budlight_timeline$text), get_nrc_sentiment)
budlight_sentiment <- data.frame('created' = budlight_timeline$created,
                               'text' = budlight_timeline$text,
                               'sentiment' = as.character(budlight_timeline$sentiment))
budlight_sentiment$score <- get_sentiment(as.character(text_clean(budlight_sentiment$text))) %>% as.numeric()
budlight_sentiment %>%
  arrange(desc(score)) %>%
  select(created, score) %>%
  tail(5)

budlight_sentiment %>%
  ggplot(aes(as_date(created), score)) +
  geom_line(size = 1) +
  geom_smooth() +
  scale_color_manual(values = colourList) +
  scale_x_date(breaks = date_breaks("3 months"), labels = date_format("%Y-%b")) +
  scale_y_continuous(name = "Sentiment\n", breaks = seq(-5, 5, by = 1)) + theme_bw() +
  ggtitle('BudLight Sentiment')

MichelobULTRA_timeline$sentiment <- lapply(text_clean(MichelobULTRA_timeline$text), get_nrc_sentiment)
michelob_sentiment <- data.frame('created' = MichelobULTRA_timeline$created,
                               'text' = MichelobULTRA_timeline$text,
                               'sentiment' = as.character(MichelobULTRA_timeline$sentiment))
michelob_sentiment$score <- get_sentiment(as.character(text_clean(michelob_sentiment$text))) %>% as.numeric()
michelob_sentiment %>%
  arrange(desc(score)) %>%
  select(created, score) %>%
  tail(5)

michelob_sentiment %>%
  ggplot(aes(as_date(created), score)) +
  geom_line(size = 1) +
  geom_smooth() +
  scale_color_manual(values = colourList) +
  scale_x_date(breaks = date_breaks("3 months"), labels = date_format("%Y-%b")) +
  scale_y_continuous(name = "Sentiment\n", breaks = seq(-5, 5, by = 1)) + theme_bw() +
  ggtitle('Michelob Sentiment')